Staff Data Scientist, Algorithm (Payments Fraud & Optimization)

About the role

ABOUT AIRWALLEX

Airwallex is the AI-native financial operating system for a real-time, intelligent economy. More than 675,000 businesses, including McLaren Racing, Qantas, SHEIN, and TikTok, use us, directly or through our platform partners, to run their financial operations or build and monetize financial products of their own.

We started in Melbourne in 2015 to build the infrastructure global commerce runs on. We're the regulated backbone behind global payments: not by accident, but by design. A decade plus, 85+ licenses, and a financial infrastructure spanning North America, Europe, the Middle East, and Asia-Pacific.

We're co-headquartered in San Francisco and Singapore, with more than 2,700 people across 27 offices. We hire builders with founder-level energy, people who move fast with good judgment, dig in with real curiosity, and make calls from first principles rather than waiting to be told what to do. Read our operating principles https://www.airwallex.com/en-us/operating-principles to see it in full.

About the Role: We are seeking a highly skilled and experienced Staff Data Scientist to drive the technical direction of our Payments Optimization team. This is a critical Individual Contributor (IC) role that will focus on designing and implementing advanced algorithms to optimize payments. You will act as a key strategic partner to Product, Engineering, and Commercial leaders, identifying complex, unique opportunities to improve payments performance and drive product adoption at scale.

Responsibilities:

- Define and drive the technical strategy for multi-objective payments optimization, ensuring that AWX optimizes payment conversion rates, fraud risk, and transaction costs.

- Act as the strong technical owner on cross-functional initiatives, solving unique, critical, and complex problems that have a broad impact on the business.

- Provide technical thought leadership on various optimization algorithms (e.g. contextual bandits) and experimentation techniques (e.g. A/B testing, causal inference).

- Collaborate with engineering teams to design and build tools and infrastructure to enable optimization at scale and deploy ML systems to production.

- Partner with Product, Engineering, Risk, and other cross-functional teams to inform and influence product strategy and investment decisions.

- Clearly and persuasively communicate complex data insights and technical recommendations to both technical and non-technical audiences, ensuring your findings influence stakeholders at all levels.

- Mentor and coach junior data scientists and engineers.

Qualifications:

- Advanced degree (PhD or MS) in a quantitative field such as Statistics, Engineering, Computer Science, Economics, or related disciplines.

- 5+ years of industry experience in data science or applied ML.

- Proven experience in experimentation and causal inference to drive data-informed product decisions with concrete impact.